GSD-2022-1650
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Improper Removal of Sensitive Information Before Storage or Transfer in GitHub repository eventsource/eventsource prior to v2.0.2.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-1650",
"description": "Exposure of Sensitive Information to an Unauthorized Actor in GitHub repository eventsource/eventsource prior to v2.0.2.",
"id": "GSD-2022-1650",
"references": [
"https://access.redhat.com/errata/RHSA-2022:5006",
"https://access.redhat.com/errata/RHSA-2022:5030",
"https://access.redhat.com/errata/RHBA-2022:5721",
"https://access.redhat.com/errata/RHBA-2022:5747",
"https://access.redhat.com/errata/RHBA-2022:5749",
"https://access.redhat.com/errata/RHSA-2022:6037",
"https://access.redhat.com/errata/RHSA-2022:6057",
"https://access.redhat.com/errata/RHSA-2022:6156",
"https://access.redhat.com/errata/RHSA-2022:6429",
"https://access.redhat.com/errata/RHSA-2022:7055",
"https://access.redhat.com/errata/RHSA-2022:6813"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
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],
"details": "Improper Removal of Sensitive Information Before Storage or Transfer in GitHub repository eventsource/eventsource prior to v2.0.2.\n\n",
"id": "GSD-2022-1650",
"modified": "2023-12-13T01:19:28.053268Z",
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}
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"date": "2023-08-02",
"description": "Exposure of Sensitive Information to an Unauthorized Actor in GitHub repository eventsource/eventsource prior to v2.0.2.",
"fixed_versions": [
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"2.0.2"
],
"identifier": "CVE-2022-1650",
"identifiers": [
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"not_impacted": "All versions starting from 1.1.1 before 2.0.0, all versions starting from 2.0.2",
"package_slug": "npm/eventsource",
"pubdate": "2022-05-12",
"solution": "Upgrade to versions 1.1.1, 2.0.2 or above.",
"title": "Exposure of Sensitive Information to an Unauthorized Actor",
"urls": [
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"uuid": "503509a0-ba05-494a-9833-502f10c72f21"
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"versionStartIncluding": "2.0.0",
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"lastModifiedDate": "2023-08-02T09:15Z",
"publishedDate": "2022-05-12T11:15Z"
}
}
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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